The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Many object recognition technologies have been developed since the advent of digital acquisition techniques. One example technique that can be used to identify objects that might appear in a digital image includes Scale-Invariant Feature Transform (SIFT) as discussed in U.S. Pat. No. 6,711,293 to Lowe titled “Method and Apparatus for Identifying Scale Invariant Features in an Image and Use of the Same for Locating an Object in an Image”, filed Mar. 6, 2000. Typically, only one algorithm is applied to a digital representation of a scene to identify or locate an object within the digital representation. Although useful for identifying objects that are amenable to the specific philosophical foundations of the algorithms, such a single minded approach is less than efficient across many different classes of objects; different types of objects across which there can be a high variability in feature density.
Some effort has been applied toward detecting object features. For example, U.S. Pat. No. 5,710,833 to Moghaddam et al. titled “Detection, Recognition and Coding of Complex Objects using Probabilistic Eigenspace Analysis”, filed Apr. 20, 1995, describes calculating probabilities densities associated with an image or portions of an image to determine if an input image represents an instance of an object. Still, Moghaddam only offers a single approach for identifying objects and fails to provide insight into classification of objects.
Substantial effort toward image processing as been applied in the field of medical imaging. European patent specification EP 2 366 331 to Miyamoto titled “Radiation Imaging Apparatus, Radiation Imaging Method, and Program”, filed Mar. 1, 2011, references calculating image density within a radioscopic image and selectively executing an extraction algorithm for reach region of interest where the density information reflects tissue density. The extraction algorithm results in features that can aid in analysis of corresponding tissue.
U.S. Pat. No. 8,542,794 also to Miyamoto titled “Image Processing Apparatus for a Moving Image of an Object Irradiated with Radiation, Method Thereof, and Storage Medium”, filed Mar. 3, 2011, also discusses image processing with respect to radioscopic imaging. Miyamoto discusses capturing a “feature amount” from pre-processed moving images where the “feature amounts” represent values derived from the image data. Thus, the feature amounts can reflect aspects of image data related to region in an image.
U.S. Pat. No. 8,218,850 to Raundahl et al. titled “Breast Tissue Density Measure” filed Dec. 23, 2008, makes further progress in the medical imaging field of extracting tissue density information from radioscopic images. Raundahl describes driving a probability score from the tissue density information and that indicates that a mammogram image is a member of a predefine class of mammograms images. Miyamoto and Raundahl offer useful instructions toward processing medical image data based on extracted features. However, such approaches are not applicable to a broad range of object types, say shoes, animals, or structured documents.
U.S. patent application publication 2008/0008378 to Andel et al. titled “Arbitration System for Determining the Orientation of an Envelope from a Plurality of Classifiers”, filed Jul. 7, 2006; and U.S. patent application publication 2008/0008379 also to Andel et al. titled “System and Method for Real-Time Determination of the Orientation of an Envelope”, filed Jul. 7, 2007, both describe using a classifier that determines an orientation of an envelope based on an image of the envelope. The orientation classifier operates as a function of pixel density, (i.e., regions having dark pixels).
U.S. Pat. No. 8,346,684 to Mirbach et al. titled “Pattern Classification Method”, filed internationally on Jul. 17, 2007, describes identifying test patterns in a feature space based on using a density function. During an on-line process, patterns can be classified as belonging to known patterns based on the known patterns having similar density functions.
International patent application publication WO 2013/149038 to Zouridakis titled “Method and Software for Screening and Diagnosing Skin Lesions and Plant Diseases” filed Mar. 28, 2013, also describes a classification system. Zouridakis discusses extracting features from regions within an object boundary in an image and comparing the extracted features to known object features in a support vector machine (SVM). The SVM returns a classification of the object.
Further, U.S. Pat. No. 8,553,989 to Owechko et al. titled “Three-Dimensional (3D) Object Recognition System Using Region of Interest Geometric Features”, filed Apr. 27, 2010, uses a feature vector to classify objects of interest. Shape features are calculated by converting raw point cloud data into a regularly sampled populated density function where the shape features are compiled into the feature vector. The feature vector is then submitted to a multi-class classifier trained on feature vectors.
U.S. Pat. No. 8,363,939 to Khosla et al. titled “Visual Attention and Segmentation System”, filed Jun. 16, 2008, discusses applying a flooding algorithm to break apart an image into smaller proto-objects based on feature density where the features represent color features derived based on various color channels. Unfortunately, Khosla merely attempts to identify regions of high saliency, possibly growing the region, rather than attempting differentiate among objects distributed across regions of interest.
U.S. patent application publication 2013/0216143 to Pasteris et al. titled “Systems, Circuits, and Methods for Efficient Hierarchical Object Recognition Based on Clustered Invariant Features”, filed Feb. 7, 2013, describes extracting key points from image data and grouping the key points into clusters that enforce a geometric constraint. Some clusters are discarded while the remaining clusters are used for recognition. Interestingly, Pasteris seeks to discard low density sets and fails to appreciate that feature density, regardless of its nature, can represent rich information.
International patent application WO 2007/004868 to Geusebroek titled “Method and Apparatus for Image Characterization”, filed Jul. 3, 2006, seeks to characterize images based on density profile information. The system analyzes images to find color or intensity transitions. The density profiles are created from the transitions and fitted to predefined parameterization functions, which can be used to characterize the image.
U.S. Pat. No. 8,429,103 to Aradhye et al. titled “Native Machine Learning Service for User Adaptation on a Mobile Platform”, filed Aug. 2, 2012; and U.S. Pat. No. 8,510,238 titled “Method to Predict Session Duration on Mobile Device Using Native Machine Learning”, filed Aug. 14, 2012, both describe a machine learning service that seeks to classify features from image data.
All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
The above cited references offer various techniques for applying some form of algorithm to image data to identify objects represented within the image data. Still, the collective references rely on a single algorithm approach to identify features within regions of interest. The references fail to appreciate that each region of interest could have a different type or class of object (e.g., unstructured documents, structured documents, faces, toys, vehicles, logos, etc.) from the other regions. Further, the references fail to provide insight into how such diverse regions of interest could be processed individually or how to determine which type of processing would be required for such regions. Thus, there is still a need for systems capable of determining which type of processing should be applied to identified regions of interest.
In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.